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ChatModel(model_id: str, endpoint_name: typing.Optional[str] = None)
ChatModel represents a language model that is capable of chat.
Examples::
chat_model = ChatModel.from_pretrained("chat-bison@001")
chat = chat_model.start_chat(
context="My name is Ned. You are my personal assistant. My favorite movies are Lord of the Rings and Hobbit.",
examples=[
InputOutputTextPair(
input_text="Who do you work for?",
output_text="I work for Ned.",
),
InputOutputTextPair(
input_text="What do I like?",
output_text="Ned likes watching movies.",
),
],
temperature=0.3,
)
chat.send_message("Do you know any cool events this weekend?")
Methods
ChatModel
ChatModel(model_id: str, endpoint_name: typing.Optional[str] = None)
Creates a LanguageModel.
This constructor should not be called directly.
Use LanguageModel.from_pretrained(model_name=...)
instead.
Parameters | |
---|---|
Name | Description |
model_id |
str
Identifier of a Vertex LLM. Example: "text-bison@001" |
endpoint_name |
typing.Optional[str]
Vertex Endpoint resource name for the model |
from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.T
Loads a _ModelGardenModel.
Parameter | |
---|---|
Name | Description |
model_name |
str
Name of the model. |
Exceptions | |
---|---|
Type | Description |
ValueError |
If model_name is unknown. |
ValueError |
If model does not support this class. |
get_tuned_model
get_tuned_model(
tuned_model_name: str,
) -> vertexai.language_models._language_models._LanguageModel
Loads the specified tuned language model.
list_tuned_model_names
list_tuned_model_names() -> typing.Sequence[str]
Lists the names of tuned models.
start_chat
start_chat(
*,
context: typing.Optional[str] = None,
examples: typing.Optional[
typing.List[vertexai.language_models.InputOutputTextPair]
] = None,
max_output_tokens: typing.Optional[int] = None,
temperature: typing.Optional[float] = None,
top_k: typing.Optional[int] = None,
top_p: typing.Optional[float] = None,
message_history: typing.Optional[
typing.List[vertexai.language_models.ChatMessage]
] = None,
stop_sequences: typing.Optional[typing.List[str]] = None
) -> vertexai.language_models.ChatSession
Starts a chat session with the model.
tune_model
tune_model(
training_data: typing.Union[str, pandas.core.frame.DataFrame],
*,
train_steps: typing.Optional[int] = None,
learning_rate_multiplier: typing.Optional[float] = None,
tuning_job_location: typing.Optional[str] = None,
tuned_model_location: typing.Optional[str] = None,
model_display_name: typing.Optional[str] = None,
default_context: typing.Optional[str] = None,
accelerator_type: typing.Optional[typing.Literal["TPU", "GPU"]] = None,
tuning_evaluation_spec: typing.Optional[TuningEvaluationSpec] = None
) -> _LanguageModelTuningJob
Tunes a model based on training data.
This method launches and returns an asynchronous model tuning job. Usage:
tuning_job = model.tune_model(...)
... do some other work
tuned_model = tuning_job.get_tuned_model() # Blocks until tuning is complete
Parameter | |
---|---|
Name | Description |
training_data |
typing.Union[str, pandas.core.frame.DataFrame]
A Pandas DataFrame or a URI pointing to data in JSON lines format. The dataset schema is model-specific. See https://cloud.google.com/vertex-ai/docs/generative-ai/models/tune-models#dataset_format |
Exceptions | |
---|---|
Type | Description |
ValueError |
If the "tuning_job_location" value is not supported |
ValueError |
If the "tuned_model_location" value is not supported |
RuntimeError |
If the model does not support tuning |
AttributeError |
If any attribute in the "tuning_evaluation_spec" is not supported |
tune_model_rlhf
tune_model_rlhf(
*,
prompt_data: typing.Union[str, pandas.core.frame.DataFrame],
preference_data: typing.Union[str, pandas.core.frame.DataFrame],
model_display_name: typing.Optional[str] = None,
prompt_sequence_length: typing.Optional[int] = None,
target_sequence_length: typing.Optional[int] = None,
reward_model_learning_rate_multiplier: typing.Optional[float] = None,
reinforcement_learning_rate_multiplier: typing.Optional[float] = None,
reward_model_train_steps: typing.Optional[int] = None,
reinforcement_learning_train_steps: typing.Optional[int] = None,
kl_coeff: typing.Optional[float] = None,
default_context: typing.Optional[str] = None,
tuning_job_location: typing.Optional[str] = None,
tuning_evaluation_spec: typing.Optional[TuningEvaluationSpec] = None
) -> _LanguageModelTuningJob
Tunes a model using reinforcement learning from human feedback.
This method launches and returns an asynchronous model tuning job. Usage:
tuning_job = model.tune_model_rlhf(...)
... do some other work
tuned_model = tuning_job.get_tuned_model() # Blocks until tuning is complete
Exceptions | |
---|---|
Type | Description |
ValueError |
If the "tuning_job_location" value is not supported |
RuntimeError |
If the model does not support tuning |